Bio-Inspired Design of Superconducting Spiking Neuron and Synapse

Author:

Schegolev Andrey E.1ORCID,Klenov Nikolay V.23ORCID,Gubochkin Georgy I.24ORCID,Kupriyanov Mikhail Yu.1ORCID,Soloviev Igor I.13ORCID

Affiliation:

1. Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia

2. Faculty of Physics, Moscow State University, 119991 Moscow, Russia

3. Faculty of Physics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia

4. Russian Quantum Center, 100 Novaya Street, Skolkovo, 143025 Moscow, Russia

Abstract

The imitative modelling of processes in the brain of living beings is an ambitious task. However, advances in the complexity of existing hardware brain models are limited by their low speed and high energy consumption. A superconducting circuit with Josephson junctions closely mimics the neuronal membrane with channels involved in the operation of the sodium-potassium pump. The dynamic processes in such a system are characterised by a duration of picoseconds and an energy level of attojoules. In this work, two superconducting models of a biological neuron are studied. New modes of their operation are identified, including the so-called bursting mode, which plays an important role in biological neural networks. The possibility of switching between different modes in situ is shown, providing the possibility of dynamic control of the system. A synaptic connection that mimics the short-term potentiation of a biological synapse is developed and demonstrated. Finally, the simplest two-neuron chain comprising the proposed bio-inspired components is simulated, and the prospects of superconducting hardware biosimilars are briefly discussed.

Publisher

MDPI AG

Subject

General Materials Science,General Chemical Engineering

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions;Nanomaterials;2024-05-14

2. Hybrid synaptic structure for spiking neural network realization;Superconductor Science and Technology;2024-05-13

3. An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks;IEEE Transactions on Applied Superconductivity;2024-05

4. Magnetic Flux Sensor Based on Spiking Neurons with Josephson Junctions;Sensors;2024-04-08

5. Eksperimental'noe issledovanie peredatochnoy funktsii prototipa sverkhprovodyashchego gauss-neyrona;Письма в Журнал экспериментальной и теоретической физики;2023-12-15

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